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Cited by 1,167

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

The Effect of Annual Report Readability on Analyst Following and the Properties of Their Earnings Forecasts

Open access
Lehavy, Reuven, Li, Feng, Merkley, Kenneth·2011·The Accounting Review

Abstract

ABSTRACT: This study examines the effect of the readability of firms’ written communication on the behavior of sell-side financial analysts. Using a measure of the readability of corporate 10-K filings, we document that analyst following, the amount of effort incurred to generate their reports, and the informativeness of their reports are greater for firms with less readable 10-Ks. Additionally, we find that less readable 10-Ks are associated with greater dispersion, lower accuracy, and greater overall uncertainty in analyst earnings forecasts. Overall, our results are consistent with the prediction of an increasing demand for analyst services for firms with less readable communication and a greater collective effort by analysts for firms with less readable disclosures. Our results contribute to the understanding of the role of analysts as information intermediaries for investors and the effect of the complexity of written financial communication on the usefulness of this information.

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Metadata

Authors
Lehavy, R., Li, F., & Merkley, K.
Venue
The Accounting Review
Published
2011-05-01
Volume
86
Issue
3
Pages
1087-1115
ISSN
  • 0001-4826 (ISSN-L)
  • 1558-7967
OpenAlex
W2081416478
Cited by
1,167
References
3
FWCI
61.90
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The Effect of Annual Report Readability on Analyst Following and the Properties of Their Earnings Forecasts. https://app.cochat.ai/papers/10.2308/accr.00000043
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Lehavy, R., Li, F., & Merkley, K. (2011). The Effect of Annual Report Readability on Analyst Following and the Properties of Their Earnings Forecasts. The Accounting Review. https://doi.org/10.2308/accr.00000043

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.